Correlation between COVID-19 and weather variables: A meta-analysis

Background COVID-19 has significantly impacted humans worldwide in recent times. Weather variables have a remarkable effect on COVID-19 spread all over the universe. Objectives The aim of this study was to find the correlation between weather variables with COVID-19 cases and COVID-19 deaths. Methods Five electronic databases such as PubMed, Science Direct, Scopus, Ovid (Medline), and Ovid (Embase) were searched to conduct the literature survey from January 01, 2020, to February 03, 2022. Both fixed-effects and random-effects models were used to calculate pooled correlation and 95% confidence interval (CI) for both effect measures. Included studies heterogeneity was measured by Cochrane chi-square test statistic Q, I2 and τ2. Funnel plot was used to measure publication bias. A Sensitivity analysis was also carried out. Results Total 38 studies were analyzed in this study. The result of this analysis showed a significantly negative impact on COVID-19 fixed effect incidence and weather variables such as temperature (r = -0.113∗∗∗), relative humidity (r = -0.019∗∗∗), precipitation (r = -0.143∗∗∗), air pressure (r = -0.073∗), and sunlight (r = -0.277∗∗∗) and also found positive impact on wind speed (r = 0.076∗∗∗) and dew point (r = 0.115∗∗∗). From this analysis, significant negative impact was also found for COVID-19 fixed effect death and weather variables such as temperature (r = -0.094∗∗∗), wind speed (r = -0.048∗∗), rainfall (r = -0.158∗∗∗), sunlight (r = -0.271∗∗∗) and positive impact for relative humidity (r = 0.059∗∗∗). Conclusion This meta-analysis disclosed significant correlations between weather and COVID-19 cases and deaths. The findings of this analysis would help policymakers and the health professionals to reduce the cases and fatality rate depending on weather forecast techniques and fight this pandemic using restricted assets.


Introduction
Coronavirus disease 2019 (COVID- 19) is an infectious disease due to coronavirus, a newly observed RNA virus that changed into previously called severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) [1,2,3]. It causes infection of the respiration tract in human beings and animals, ensuing in fever, cough, and cold. Patients may die from acute respiratory distress syndrome or pneumonia [4]. Coronaviruses are beta coronaviruses that belong to the Orthocoronavirinae subfamily and the Coronaviridae family. Coronavirus receives its call from the Latin phrase corona, which means that crown or wreath. Coronaviruses were first detected within the 1930s in North Dakota in domesticated chickens with an acute respiratory tract infection [3].
HCoV-229E, HCoVOC43, HCoV-HKU1, and HCoV-NL63 are seven coronavirus species recognized to contaminate human beings and cause disease. They are regularly mild and cause typical cold symptoms.
Middle East respiratory syndrome-associated coronavirus (MERS-CoV), excessive acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the other three human coronaviruses that motive probably severe symptoms and have been observed in 2012, 2002, and 2019 [5,6]. The Chinese Center for Disease and Prevention diagnosed the first instance of COVID-19 from a patient's throat swab on December 8, 2019, in Wuhan, Hubei Province, China [7]. Since the improvement of the COVID-19 disease in China, it has speedily grown into a worldwide hazard, and the World Health Organization has categorized it as a pandemic (WHO).
This disease has unfolded in 228 nations and territories worldwide, with 544, 433, 589 confirmed cases and 6,341,131 deaths (World Health Organization statistics as of June 20, 2022). The mortality rate is high worldwide due to COVID-19 infection. The mortality rate per 1 million people is approximately 813.5 [7,8].
The highest confirmed COVID-19 cases and deaths were reported in the United States of America, with 88, 004, 073 and 1,038,323, respectively. The second highest confirmed cases have been reported in India, with 43, 311, 049 confirmed cases. The third highest deaths were recorded at 525,873. The third highest confirmed cases have been reported in Brazil, with 31,704,193 cases, and the second-highest deaths are 669,109 [8]. In the European region, highest number of confirmed cases was found from France (30,079,458), Germany (27,211,866), UK (22,472,503), Russia (18,400,927), South Korea (18,280,090), Italy (17,896,065), Turkey (15,085,742) as well as highest number of death were reported in European region from Russia (380,517), UK (179,537), Italy (167,780), France (149,039), Germany (140,292), Poland (116, 393) and Spain (107,482) [8]. In the African region, the majority of confirmed cases/death were found in South Africa (3,986,601/101,604), and in the Eastern Mediterranean region, the highest cases/death were found in Iran (7,234,988/141,366). India reported highest number of cases/deaths in South-East Asia region. The confirmed number of cases in Bangladesh was 1,957,200, with 29,131 deaths from June 20, 2022 [8].
About 66.3% of the world population has received at least one dose of a COVID-19 vaccine. 11.99 billion doses have been administered globally, and 7.25 million are now administered daily. The highest number of vaccinated covered by China, 3.4 billion and followed by India (1.96 billion), USA (592.27 million), Brazil (449.34 million), Indonesia (417.52 million), Japan (287.42 million), Bangladesh (275.48 million), Pakistan (259.29 million) and so on. Only 17.8% of people in low-income countries have received at least one dose [9].
The association of COVID-19 with meteorological parameters such as humidity, temperature, precipitation, wind speed, air pressure, rainfall, and sunlight has been examined in previous studies [10,11,12,13,14,15]. However, a weak association between COVID-19 and weather variables was found in the available studies [16,17]. A bidirectional relationship between COVID-19 and weather variables was also seen in a previous systematic review [18]. The recent meta-analysis and systematic review result also found a significant association between weather variables and COVID-19 transmission [19,20]. Several studies reported that maximum temperatures have low effects on COVID-19 transmission [11,21,22] while contradicting results were also found [23]. Weather variables, including temperature, must be appreciably undoubtedly positively correlated with the transmissibility of COVID-19 in Singapore, Brazil, Indonesia, Japan, and Norway but significantly negatively correlated in New York City, Iran, Bangladesh, and China [24]. One study revealed that other weather variables like relative humidity (RH) and rainfall were negatively associated with new daily cases and deaths [25,26,27]. A positive association was found between COVID -19 cases and relative humidity in a study [28], whereas no correlation between them was also seen in another study [29]. A researcher has also proven a significant relationship between COVID-19 cases and wind speed. The impact of wind speed has a significant positive effect on COVID-19 cases [30,31]; however, a negative result was also found [32]. Another literature review concluded that sunlight significantly impacts covid 19 cases [27]. The relationship between weather variables and COVID-19 transmission will also be due to season modifications and geographical location [33,34,35]. Due to conflicting findings about the association between weather variables and COVID-19 cases and deaths, it is imperative to compile all available data for study in order to identify a consistent effect of weather variables on the COVID-19 cases and deaths. Based on the previous literature, it may be said that the discrete conclusion is yet to be drawn on the prospective role of the weather variables on COVID-19 worldwide. Therefore, it wishes more studies on this subject matter in different world regions, including Bangladesh.
The purpose of this study is to review the correlation between weather variables (such as temperature, rainfall, relative humidity, precipitation, dew-point, air pressure, wind speed, and sunlight) and COVID-19 cases as well as deaths and to review the existing findings in a metaanalysis.

Methods
This study has shown a Meta-analysis of the impacts of weather variables (temperature, relative humidity, rainfall, wind speed, precipitation, air pressure, dew point, sunlight) on the daily number of  confirmed COVID-19 cases COVID-19 death. Thus, this study performed  proper inclusion and exclusion based on available literature on the correlation between weather variables and the COVID-19 cases/COVID-19 deaths. After exclusion and inclusion from the selected relevant articles, this study considers the average value of the following weather variables: temperature, relative humidity, rainfall, wind speed, precipitation, air pressure, dew point, and sunlight to find the correlation between death and incidence of the COVID-19. The study performed forest and funnel plots to investigate heterogeneity and publication bias. Lastly, the study conducted a sensitivity analysis to find the most prominent study on the pooled result.

Search strategy
Five electronic databases such as PubMed, Science Direct, Scopus, Ovid (Medline), and Ovid (Embase) were searched to conduct the literature survey from January 01, 2020, to February 03, 2022, using a set combination of keywords to search the desired articles. The detailed search strategy of different databases is described in supplementary files (Appendix A). As for example, the search term for

Eligibility criteria of the study
This study included the articles assessing the correlation between the average value of the weather variables (temperature, relative humidity, rainfall, wind speed, precipitation, air pressure, dew point, and sunlight) and COVID-19 incidence, as well as the correlation between weather variables and COVID-19 deaths as the primary outcome of interest. Articles that reported a correlation between weather variables and COVID-19 cases/incidence or COVID-19 deaths/fatalities were included in this study. Studies that didn't report any correlation between the weather variables and the COVID-19 cases or COVID-19 deaths were excluded from this study. This analysis considered cross-sectional, time series, or case study designs. Randomized controlled trials, cohort studies, casecontrol studies, case report study designs, letters to editors, systematic review articles, editorials, and short communication were excluded from this research. This study only considered articles that included daily confirmed COVID-19 cases, deaths, and weather variables. Human-based studies and English language writing articles were included in this study. This study has included only peer-reviewed and published articles and excluded unpublished articles due to data uncertainty.

Data extraction process and study quality assessment
In Rayyan-Intelligent Systematic Review software, all identified possible articles were entered. After entering all the articles in the software, duplicate articles were detected and then removed the duplicate one. After removing the duplicate, two reviewers independently screened the title and the abstract based on the search strategy. Those articles selected for inclusion were finally full-screened by the two independent reviewers. Controversial matters were resolved after discussion. The extracted data included based on weather/weather parameters (such as temperature, relative humidity, rainfall, wind speed, precipitation, air pressure, dew point, sunlight) and the daily number of COVID-19 cases as well as deaths, author name, country, place of data collections, year of publication, time, and study design. PRISMA checklist was used to present the results of this analysis [36]. Using the Joanna Briggs Institute (JBI) tool to assess the quality of the articles [37]. JBI tools contain a total of eight questions, such as; Q1: Where were the criteria for inclusion in the sample clearly defined, Q2: Were the study subjects and the setting described in detail, Q3: Was the exposure measured validly and reliably, Q4: Were objective, standard criteria used for measurement of the condition, Q5: Were confounding factors identified, Q6: Were strategies to deal with confounding factors stated, Q7: Were the outcomes measured validly and reliably And Q8: Was appropriate statistical analysis used. All the questions from JBI were examined against all the articles. In this study, the answer was taken in dichotomous "Yes (1)" and "No (0)". Overall quality was assessed with 6, and above "yes" responses, then it was considered a high-quality publication, 4 and 5 considered moderate. Less than 4 were deemed low-quality publications from the 8 points [38]. A quality assessment table for all included articles was given in the supplementary file (Table S1).

Statistical analysis
Data analysis was conducted using R programming software and Microsoft Excel. The pooled correlation (r) and 95% confidence interval (CI) were calculated using both fixed effects and random effects models [39,40]. The pooled correlation was used to calculate the correlation between weather variables and the COVID-19 cases and deaths.
Chi-square test statistics (Q), I 2 , and τ 2 test was used in this analysis to examine the between-study heterogeneity [41]. To see the heterogeneity among the included studies, this study used forest plots. Existing heterogeneity was identified through subgroup analysis based on the continent. A single study's effect on the overall result was observed by doing sensitivity analysis. By using a funnel plot, publication bias is detected [42].

Search results and study characteristics
960 articles were found from five databases Scopus, Science Direct, PubMed, Ovid (Medline), and Ovid (Embase). Of the total articles, 523 articles were identified after duplication was removed, and from those remaining articles, 158 articles were identified through title and abstract. One hundred fourteen articles were selected for full-text assessment, while 76 articles were excluded due to lack of proper information. Finally, 38 publications were included in this meta-analysis ( Figure 1). The characteristics of the included studies are detailed in (Table 1). The articles included studies in countries worldwide belonging to Bangladesh, India, Indonesia, China, Jordan, Malaysia, Japan, Spain, Italy, USA, Norway, Poland, Brazil, Saudi Arabia, Singapore, Taiwan, Russian federation, UK, Australia, and Africa. The article mainly used Spearman's rank correlation and Pearson's correlation coefficient values for analysis purposes. A cross-sectional time series or case study was included in this article.

Subgroup analysis
The correlation between weather variables and COVID-19 incidence and death regarding the continent was presented in the subgroup analysis (Table 3). 34 studies were found about the correlation between COVID-19 incidence and temperature on a different continents. The highest negative correlation was found in South America -0.729*** (95% CI: -0.901, -0.557) and lowest in North America -0.140*** (95% CI: -0.243, -0.037). Positive correlation was found for COVID-19 incidence and temperature in Asia 0.016*** (95% CI: -0.022, 0.055) (Fig. S14). The overall correlation between relative humidity and COVID-19 incidence was significant. Heterogeneity of this correlation was found continentwise. In Asia, the correlation was significant and negatively correlated -0.056*** (95% CI: -0.096, -0.016) (Fig. S15). Similarly, the overall correlation between wind speed and COVID-19 incidence was significantly positively correlated. Heterogeneity of this correlation was found continent-wise. The correlation was significantly positive in Asia, but in South America, it was significantly negatively correlated (Fig. S16). Heterogeneity was found among different continents for rainfall and COVID-19 incidence (Fig. S17). Overall precipitation was negatively associated with COVID-19 incidence, and a higher negative correlation was found in Asia (Fig. S18). Widespread precipitation was negatively correlated with COVID-19 incidence, but heterogeneity among the continent was insignificant (Fig. S19). Dew point was positively correlated with COVID-19 incidence; continent-wise, in Europe, it was found to be negative, but in Asia, it was found to be a positive correlation (Fig. S20). The highest negative correlation between COVID-19 cases and sunlight was found in Europe -0.696*** (95% CI: -0.832, -0.560) (Fig. S21). The correlation between weather variables (relative humidity, wind speed temperature, and rainfall) and deaths was significant. Regarding the correlation between temperature and death, South America was highly negatively correlated -0.820*** (95% CI: -1.032, -0.607), but on the other hand, North America was highly positively correlated at 0.403*** (95% CI: 0.275, 0.530) (Fig. S22). Interesting findings were also found for the variable relative humidity. The correlation was significantly negatively correlated in Asia and Africa, but in Europe, it was significantly positively correlated (Fig. S23). South America found the highest negative correlation between wind speed and death   -0.316*** (95% CI: -0.529, -0.104) (Fig. S24). For rainfall variables, South America and Asia were significantly negatively correlated with death (Fig. S25).

Publication bias and sensitivity analysis
A funnel plot was used in this analysis to detect publication bias. All variables showed publication bias evidence except COVID-19 incidence and air pressure, COVID-19 death, and dew point (Fig. S26-S39). Sensitivity analysis was used to find the most prominent study on the overall estimates. According to sensitivity analysis, there was no dependence on any one study for the overall estimates of COVID-19 cases and deaths (Fig. S40-S53).

Discussion
COVID-19 cases and deaths are influenced by social activities that are frequently temperature sensitive. On cold and hot days, people prefer to stay at home, whereas on pleasant days, they prefer to go outside. Temperature variations may indirectly impact COVID-19 incidence and death because the virus spreads more easily in confined spaces. Because viral aerosol dispersal is likely influenced by humidity and temperature, humidity is an essential meteorological factor in COVID-19 transmission. Lowen et al. (2007) conducted 20 experiments at different relative humidity levels ranging from 20% to 80% and temperatures (5 C, 20 C, or 30 C), indicating that both cold and dry conditions favor transmission [79]. Temperature and relative humidity, wind speed, and sunlight are the most crucial weather variables that are strong enough to impact the death and incidence of COVID-19. Correlation parameters were applied to disseminate a clear connection between the weather and COVID-19 in each study that was included. Aside from the heterogeneity and dispersion of actual size effects, this study has an advantage over the fixed and random effect models. Significant forest plots were obtained for the temperature versus the incidence of COVID-19, temperature versus death, relative humidity versus the incidence of COVID-19, relative humidity versus death, precipitation versus the incidence of COVID-19, precipitation versus death, air pressure versus the incidence, dew point versus the incidence, dew point versus death, sunlight versus the incidence, and sunlight versus the death. To ascertain the cause of variance in COVID-19 cases and deaths due to geography, subgroup analysis was done with regards to the continent.
The highest negative correlation between temperature and incidence was found in South America, followed by Africa, Europe, and North America. A significant negative correlation was also found in several studies [31,46,51], but a positive correlation between temperature and COVID-19 incidence was found in Bangladesh [52]. The correlation between incidence and relative humidity has been significantly negative in Asia. Italy, Africa, and Saudi Arabia have also found a negative correlation between COVID-19 incidence and relative humidity [61,66,67], but a positive correlation was found in Malaysia, Singapore, Thailand, and Bangladesh [52,58,65,71]. An exciting finding was also seen for the correlation between wind speed and incidence, with a positive correlation in Asia and the negative correlation in South America. A negative correlation was also found in Brazil and China [31,63,70], and a positive correlation was found in Japan, Bangladesh, and Thailand [50,52,71]. In Europe and South America, a negative correlation was found between rainfall and incidence. Only in Asia precipitation was negatively correlated with the incidence. A researcher also found a negative correlation between incidence and precipitation in China [70]. In correlation between dew point and incidence, a significant positive correlation was found in Asia and negative in Europe. The highest negative correlation between sunlight and incidence was found in Europe, followed by Asia. Sunlight was also negatively correlated with Indonesia's incidence [32,43]. A positive correlation between death and temperature in Asia and North America was found, but a negative correlation was found in Africa and Europe [51,61]. In this study, the correlation between relative humidity and death was negative in Asia and Africa, but in Europe, it was positive. But MEO SA et al. found a negative correlation between relative humidity and death in Europe [51]. The correlation between Wind speed and death was positive in Asia, but a negative correlation was found in South America and Europe. The correlation between rainfall and death was negative in South America and Asia. An exciting finding was noticed in this study, pooled correlation of incidence and death to the weather variables. Wind speed and incidence were negatively correlated, but wind speed and death were positively correlated. This means the incidence increases, and the death decreases with the increase of wind. In Asia, the death increases but the incidence decrease with the temperature increases. The incidence and death decrease with the increase of relative humidity and incidence and death increase with the increase in wind speed in Asia. In Europe, temperature increases, and the death and incidence decrease. In North America, temperature increases the death increase but incidence decreases. In South America, the incidence and death decrease with increasing temperature, wind speed, and rainfall. In Africa, the incidence and death decrease with the increase in temperature.
A general conclusion is yet to be drawn regarding the correlation between weather variables and COVID-19 incidence and death. Different results have been shown in different continents for a single variable. In order to stop the COVID-19 epidemic, this study suggested that weather experts and medical professionals look more attentively at the data of this paper. To more effectively bolster the findings of our meta-analysis, more study should be done.

Strengths and limitations
There are few publications available summarizing weather and COVID-19 research. This study aimed to investigate the relationship between COVID-19 incidence and mortality and weather variables. The study's strength in including data from several global studies and information from other parts of the world helps us better understand global geographical distribution of transmission and how they interact with weather factors in varied climates. This analysis has also some limitations. English language articles were only included in this study. There are several limitations on weather correlation with COVID-19 cases and death due to the unavailability of much research. Biasness and high heterogeneity were also found in the included studies. Some weather variables could not be included in the analysis due to insufficient of data. Publication bias was also observed. However, this study suggests more research on the correlation between weather and COVID-19 cases and death.

Conclusion
The relation between COVID-19 incidences and meteorological conditions is complex. The global reach of a pandemic and other factors involved in the COVID-19 pandemic, such as healthcare interventions, public health measures, human behavioral patterns, and socioeconomic factors, make it difficult to examine relationships and correlations with weather variables and COVID-19 incidence dynamics. The majority of studies have found strong correlations between COVID-19 cases and meteorological factors, particularly temperature and humidity, demonstrating the influence of the weather and environment on transmission dynamics. Other factors, including as societal behavior and public health activities, may have a significant impact on future outbreaks, despite the fact that changes in seasonal patterns and weather may increase the incidence and mortality of COVID-19 [79]. This study has found a significant correlation between weather variables and COVID-19 cases and death. Those results would allow the health care specialists or the government's policymakers to make earlier choices before the projected rise in COVID-19 cases based on the weather forecasting technique.

Author contribution statement
Md. Momin Islam, MS; Farha Musharrat Noor: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement
Data will be made available on request.

Declaration of interests statement
The authors declare no conflict of interest.

Additional information
Supplementary content related to this article has been published online at https://doi.org/10.1016/j.heliyon.2022.e10333.